2. Operations Research (OR)
Overview of the OR Modeling Approach
1. Carefully observing and formulating the problem, gathering all relevant data
 Crucial, because affects relevance of the conclusions (it’s difficult to extract a right answer from the wrong problem)
2. Constructing a mathematical model to abstract the essence of the real problem
 Decision variables: x1, x2, …, xn
 Objective function: P (profit) = 3x1 + 2x2 + … + 5xn
 Constraints: x1 + 3x1x2 + 2x2, ≤ 10 restrictions on the values to decision variables
 Parameters: the constants (namely, the coefficients and righthand sides) in the constraints and objective function
 Sensitivity analysis: analysis of changes in the solution depending on the values of parameters,
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Testing the model and refine it as needed
 Retrospective test use historical data to determine how well the model and the resulting solution would have performed if they had been used, and using alternative solutions from the model on it predicts the relative effects of alternative courses of actions
6. Prepare for the ongoing application of the model as prescribed by management
 Databases and management information systems may provide uptodate input for the model each time it is used, in which case interface programs are needed
 Decision support system is installed to help managers use data and models to support (rather than replace) their decision making as needed
7. Implementing the system
 Support of upper management (feedback)
 Training for personnel who will use the system
Introduction to Linear Programming
Graphical method (especially for two decision variables only, but also possible for three)
 feasible region is the region of the graphic where the solutions (considering the constraints) can be, that is, the set of permissible values for the decision variables
Standard Form of the Model
 there are functional constraints (or structural constraints) and non negativity constraints (or nonnegativity conditions)

Get the most out of the constraint when it is identified, or “exploit” it. The constraint must always be managed in specifically working this part of the process.
The diagram below shows the feasible region of the intersection of two lines. This means that any point within the feasible region satisfies all constraints that we established before graphing. Feasible regions make it easier for us to determine the maximum profit and now we know all the possible combinations it’s important to know what point on the graph is going to be the most profitable.
Constraints are everything that influences the rhetoric or context of the writing. I suppose you could argue the exigence is the most important but I believe the constraints are what make the essay or article what it should be. If the exigence is the purpose then the constraints are what controls where the purpose goes and what is written or said by the rhetor and what the audience hears or reads. Constraints aren’t necessarily a bad thing or even limiting, I look at it as more understanding the limits of my audience and the reason for writing and the effect it will have on my audience. For this paper in particular without going into understanding the English language and all that one of my constraints would have been the topic. It would have made no sense to talk about horse pedigree in a paper about metal art so my writing had to stay on topic. Another constraint would be I assumed the readers were at least familiar with the trade so my acronyms were understood. While analyzing the paper I can’t forget to mention the kairos of my
The following linear programming problem has been written to plan the production of two products. The company wants to maximize its profits.
16) In an unbalanced transportation problem where total demand exceeds total supply, the demand constraints will typically have "≤" inequalities.
B) if the righthand side value of the constraint increases by 1 unit, the objective function value will decrease by 5 units
When considering operational constraints we looked at people, location, premises, equipment, money, materials, other related activities and services.
If we are solving a 01 integer programming problem, the constraint x1 ≤ x2 is a conditional constraint.
Decision Support Systems promote a learning culture in an organization. Employees and managers learn new concepts and more efficient ways of improving the organization, either as a byproduct of the application of DSS or from the direct implementation of DSS as training tools. This learning process is good for the company’s future decision making processes.
Sensitivity analysis allows a change in one particular variable of simulation. This shows how a project is affected by the change. It shows us what can happen in a project with different input
The main goal of my Decision Science course is to equip executives or any decision maker with tools to deal with the decision making process. The course provides us with a systematic, coherent approach to help with problem solving.
The objective function, decision variables and constraints are fed into solver to arrive at the optimal solution as shown in the below screenshot
The reduced cost for S11, S41, S12, S22, and S32 is above zero because in the solution these values are zero, so increasing the “final value” of these trucks or leasing one of any of these trucks would lead to an increase in cost of 3515, 3515, 3725, 210 and 915, respectively. This also means that the cost would have to decrease by those respective numbers in order for the optimal solution to include those variables.
The objective function to be minimized i.e, the overall cost function of the generator would be the sum of cost function of each
The first constraint is the maximum meals which would be prepared each night. The decision makers wanted to set a fixed maximum so they can get the right amount of ingredients and not produce any extra waste. The set number has been decided by the decision